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All Journal Farmaka E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal STIE Semarang (Edisi Elektronik) Syntax Idea El-Waroqoh : Jurnal Ushuluddin dan Filsafat Jurnal RSMH Palembang Jurnal Teknologi Industri Jurnal Media Akademik (JMA) Digital Innovation : International Journal Of Management El-Waroqoh : Jurnal Ushuluddin Dan Filsafat Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Achmad Junaidi
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Klasifikasi Penyakit Kronis Melalui Mata Menggunakan Algoritma Convolutional Neural Network Dengan Model MobileNet-V3 Mohammad Haydir Awaludin Waskito; Andreas Nugroho Sihananto; Achmad Junaidi
Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Vol. 2 No. 2 (2024): Juni: Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/uranus.v2i2.120

Abstract

Chronic diseases in humans are very difficult to detect visually, for example glaucoma, hypertension, diabetes, and others. So it takes a lot of time for further medical examination by visiting a health center or hospital. Therefore, this research aims to find a solution combining medical and computer science to classify quickly and precisely. Classifying eye images requires good features and characteristics so that disease images can be classified. This research uses the Deep Learning method, namely Convolutional Neural Network with MobileNet-V3 architecture which can extract features from large resolution images very well. This research resulted in accurate classification of images of chronic diseases Normal, Diabetes, Glucoma, Cataract, Age related macular degeneration, Hypertension, Pathalogical Myopia. uses the MobileNet-V3 architecture, with transfer learning reaching 81%, and loss only 0.4913.
IMPLEMENTASI EKSTRAKSI FITUR TEKSTUR DAN WARNA PADA KLASIFIKASI PENYAKIT DAUN JAGUNG MENGGUNAKAN RANDOM FOREST Galan Ahmad Defanka; Achmad Junaidi; Hendra Maulana
Prosiding Seminar Nasional Sains dan Teknologi Vol. 15 No. 1 (2025): Prosiding SNST 15 Tahun 2025
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v15i1.14646

Abstract

Penelitian ini mengembangkan sistem untuk mengidentifikasi penyakit pada daun jagung menggunakan kombinasi ekstraksi fitur tekstur dan warna. Ekstraksi tekstur dilakukan dengan metode Local Binary Pattern (LBP), sedangkan karakteristik warna citra dianalisis menggunakan Fuzzy Color Histogram (FCH). Fitur yang dihasilkan dari kedua metode tersebut kemudian digunakan sebagai input pada algoritma Random Forest untuk proses klasifikasi. Dataset berupa citra daun jagung melalui beberapa tahapan pengolahan, yaitu preprocessing, segmentasi citra, ekstraksi fitur, serta pelatihan model. Hasil pengujian menunjukkan bahwa Random Forest memiliki performa terbaik dengan akurasi mencapai 93,75%. Penelitian ini membuktikan bahwa kombinasi fitur tekstur dan warna dengan algoritma Random Forest efektif dalam mendeteksi penyakit daun jagung. Kata kunci: daun jagung, ekstraksi fitur, random forest
Co-Authors Agus Kharir Andreas Nugroho Sihananto Anggraeni, Dya Anisa Rohmanita Dewi Anissa Putri Amelia Asep Riswandi Damayanti, Elok Galan Ahmad Defanka HINDARDI HINDARDI Ika Sea Puspita Lukman Hakim Lukman Hakim Maulana, Hendra Mohamad Rifqy Roosdhani Mohammad Haydir Awaludin Waskito Muhammad Hasnawi Haddani Pinto D Ramadhoni Puspita, Ika Sea Silvia Hendrayanti SUNARTO Wanuri Wanuri Zelika Mega Ramadhania